Cardiovascular Data Quality Assessment Using Motion Contact Placement Models
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Solution Overview
Problem
Current systems for determining cardiovascular parameters face challenges in ensuring data quality, leading to unreliable outputs, and lack efficiency in real-time data quality determination, which can impact the accuracy of cardiovascular assessments.
Innovation Solution
A system and method that utilize a user device and computing system with a data quality module, comprising a motion model, body region contact model, and placement model, to assess the quality of plethysmogram data by classifying motion, contact, and placement parameters, ensuring high-quality data for accurate cardiovascular parameter determination.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If real-time data quality determination is implemented, then reliability of cardiovascular parameter determination is improved, but device complexity increases
Solution Approach 1:
The data quality module is divided into three separate models: motion model, body region contact model, and placement model. Each model independently evaluates specific aspects of data quality, and their outputs are combined to determine overall data quality. This segmentation reduces the complexity of a single comprehensive model while maintaining reliable cardiovascular parameter determination.
2Measurement precision
If multiple models are used for data quality assessment, then measurement precision is improved, but computational efficiency deteriorates
Solution Approach 1:
The system performs preliminary data quality assessment using the three models before proceeding to cardiovascular parameter determination. By evaluating motion, contact, and placement parameters in advance, the system ensures high measurement precision while maintaining computational efficiency through staged processing.
Solution Approach 2:
The system dynamically adjusts processing based on data quality outcomes. High-quality data proceeds directly to parameter determination, while low-quality data triggers feedback for recollection, optimizing computational resources by avoiding unnecessary processing of poor-quality data.
3Loss of information
If data quality checking is performed, then loss of information is reduced, but time consumption increases
Solution Approach 1:
The system performs self-validation through automated data quality assessment using the three models, reducing information loss by identifying and flagging poor-quality data. The feedback mechanism enables self-correction through guided recollection, minimizing time loss by efficiently targeting only the necessary data recollection rather than comprehensive re-measurement.
Data Source
AI summary
The system for cardiovascular parameter data quality determination can include a user device and a computing system, wherein the user device can include one or more sensors, the computing system, and/or any suitable components. The computing system can optionally include a data quality module, a cardiovascular parameter module, a storage module, and/or any suitable modules. The method for cardiovascular parameter data quality determination can include acquiring data and determining a quality of the data. The method can optionally include processing the data, and/or determining a cardiovascular parameter, training a data quality module, any suitable steps.